Why retail is embracing machine learning.

The retail sector is tough. And it’s getting tougher as the consumer gains more power.

As we have pointed out in our previous blogs the consumer is king; they have more choice than ever before and the ability to use so many channels to research, compare price and service and ultimately make a decision before they make a purchase. The path to purchase is complex and varied and changing each and every time for each and every consumer.

Retailers have embraced technology broadly; investing in their physical stores and right across their supply chain. Their digital strategies have had to evolve at breakneck speed to account for the rise of customer demand; and the proliferation of new means of access. Customers have evolved from wanting to browse stock online to seeking the means to browse, compare, order, collect at the store of their choice or delivery method of their choice in minutes. Retail has embraced mobile, social, new delivery and collection methods – the logistics behind all of this are massive.

As the online interaction has increased retailers have now got access to huge amounts of data on their customers’ behaviour and their own performance in meeting the demands before them. Consumer information gathered includes their shopping habits, browsing info, social media usage, purchase history and interaction with marketing methods. One of the major challenges retailers now face is the sheer volume of data they are collecting and the ability to analyse and make sense of it. Many different types of analytics have been deployed to provide different views on data for retailers; however increasingly retailers are turning to machine learning solutions.

Only machine learning solutions can handle the sheer volume and scale of data retailers have gathered and indeed add to every second/minute/hour every day! Machine learning algorithms are being embedded into the sourcing, buying and supply chain for retailers; whereby they can incorporate structured and unstructured data from 3rd party providers or social networks to predict more accurately customer demands for the upcoming season. In ‘predicting’ demand more accurately retailers can drive improved margins through sourcing the right amount and mix of goods to sell. By deploying machine learning across all these areas retailers can understand all the drivers for demand.

However getting the demand more accurate is only half the story; you then need to align your marketing efforts to drive sales of those goods; if something doesn’t sell it costs money. Retailers are fighting hard to engage with the consumer on their path to purchase to win their business; this is the tough part for retailers when the consumer has so much choice. Machine learning methods are helping forward thinking retailers gain that critical edge in ensuring their sales and marketing efforts win them business.

Programmatic online advertising methods are rising in popularity; all driven by machine learning algorithms matching online behaviours with an action; though this is still in its infancy and usually means a targeted ad being served. The speed at which a machine learning solution can understand a context, based on a number of data sources and execute a preset action offers retailers the opportunity to drive a 1:1 engagement with their customers beyond a simple personalised email template.

Retailers have all of this data on customer behaviour, past purchase history and spend a lot of time working out which message is right for which customer and at what time. They are aligning their sales efforts to keep a customer loyal, engaged and spending. Machine learning solutions offer them the ability to do so much more. Rather than simply predict intent based on the past why not also find it in the moment. That is what we did when we built our ‘Path 2 Purchase’ product using advanced patent pending machine learning methods. We process at scale to find purchase intent across a number of social data networks; once found we allow our partners to process that lead, interest or consideration for the retailer to act in the moment to win a sale. Retailers are embracing not only how this can be used to keep existing customers loyal but to also identify if they are considering moving to a competitor.

Most of all they can take ACTION in the moment and encourage or close a sale there and then; in real time; in the seconds the consumer has ‘devoted’ their attention to their own path to purchase. To do this they need technology capable of turning round a classification decision in real-time; we’ve got the stats to deliver this:

For more information please contact Andrew Watson, VP Strategic Alliances, Andrew@chatterbox.co or visit our web site for more information www.chatterbox.co